DEVICE FOR DETECTING VARIANT MALICIOUS CODE ON BASIS OF NEURAL NETWORK LEARNING, METHOD THEREFOR, AND COMPUTER-READABLE RECORDING MEDIUM IN WHICH PROGRAM FOR EXECUTING SAME METHOD IS RECORDED

The present invention provides an apparatus for detecting variants of malicious code based on neural network learning, a method therefor and a computer readable recording medium storing a program for performing the method. According to the present invention, one-dimensional binary data is converted into two-dimensional data without separate extraction of features, and deep learning is performed through a neural network having a nonlinear multilayered structure, such that the features of the malicious code and variants thereof may be extracted by performing the deep learning. Therefore, since no separate feature extraction tool or artificial effort by an expert is required, an analysis time is reduced, and variants of malicious code that cannot be captured by existing malicious code classification tools may be detected by performing the deep learning.